{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banner_Top_06.06.18.jpg?raw=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Data to a Jupyter Notebook\n",
    "\n",
    "This Notebook will walk you through the basic process of how to import data from Text files (.txt) and Excel files (.xls or .xlsx).\n",
    "\n",
    "In order to complete this activity, you need to first upload your data set (e.g. Sample Data.txt or Sample Data.xlsx) into your Callysto Hub (hub.callysto.ca) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing a .txt file:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we need to import \"pandas\", which is a library that contains many useful tools for working with data.  Pandas is a short form for \"Python Data Analysis Library\".  You only need to include this line once, before the rest of your code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, use the line below to read the file and assign it the variable name \"dataset\". This name can be anything you choose, and can be used to refer to the data from now on.\n",
    "\n",
    "This code assumes the columns in your data set are separated by an indented space (known as a \"tab-delimited file\").  If your columns are separated by commas, you will need to replace sep = \"\\t\" with sep = \",\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = pd.read_csv(\"Sample Data.txt\", sep = \"\\t\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can simply use the variable name to display your data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Temperature\n",
       "0  2014         24.2\n",
       "1  2015         25.3\n",
       "2  2016         25.6\n",
       "3  2017         25.8\n",
       "4  2018         26.1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that our data is loaded into the notebook, we can perform simple calculations.\n",
    "\n",
    "For example, if we wanted to find the maximum of the numbers in the second column, we do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26.1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Temperature\"].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if we wanted to figure out the average temperature during this 5-year period, we do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.4"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset[\"Temperature\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing an Excel spreadsheet:\n",
    "\n",
    "Importing an Excel spreadsheet is virtually identical to importing a text file. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>24.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>25.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>25.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018</td>\n",
       "      <td>26.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Temperature\n",
       "0  2014         24.2\n",
       "1  2015         25.3\n",
       "2  2016         25.6\n",
       "3  2017         25.8\n",
       "4  2018         26.1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exceldata = pd.read_excel(\"Sample Data.xlsx\")\n",
    "\n",
    "#Displaying the data:\n",
    "exceldata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the Excel file is loaded, we can perform calculations using it just like before. This time, let's find the maximum and average values in the first column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exceldata[\"Year\"].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2016.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exceldata[\"Year\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Your Data\n",
    "\n",
    "First we'll create a BAR graph, followed by a LINE graph.  To do this, we first need to import the \"matplotlib\" library which enables us to create plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(exceldata[\"Year\"], exceldata[\"Temperature\"])\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Global Temperature')\n",
    "plt.title('Evidence of Climate Change')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(exceldata[\"Year\"], exceldata[\"Temperature\"],marker='o')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Global Temperature')\n",
    "plt.title('Evidence of Climate Change')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For detailed information on what you can do with the Pandas Library, check out these references.\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/tutorials.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](https://github.com/callysto/callysto-sample-notebooks/blob/master/notebooks/images/Callysto_Notebook-Banners_Bottom_06.06.18.jpg?raw=true)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
